Two‐dimensional shallow water models have been widely used in forecasting, risk assessment, and management of floods. Application of these models to large‐scale floods with high‐resolution terrain data significantly increases the computation cost. In order to reduce computation time, shallow water models are simplified by neglecting the inertial and/or convective acceleration terms in the momentum equations. The local inertial models have proved to significantly improve the computational efficiency even for large‐scale flood forecasting. However, instability issues are encountered on smooth surfaces of urban areas having low friction values. This problem was resolved by de Almeida et al. (2012, https://doi.org/10.1029/2011WR011570) by introducing limited artificial diffusion in the form of weighting factors for the neighboring fluxes. The arbitrary value of the weighting factor poses a practical limitation of being case specific and requiring calibration for accurate solutions. This study derives an explicit expression for the weighting factor, an adaptive formulation dependent on local velocity, flow depth, grid, and time step size, which eliminates the need for trials and approximations. Comparisons between analytical, experimental, and real‐world applications confirm the accuracy and robustness of the proposed weighting factor. Implementation of adaptive weights results in less computation time compared to LISFLOOD‐FP (~1.2 times) and holds a significant advantage over HEC‐RAS (~25.9 times) as it allows the use of larger time step at higher Courant‐Friedrichs‐Lewy (CFL) values. The contribution of the present study therefore resolves an important problem of current large‐scale flood simulations, especially those implemented in real time.

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The quantitative analysis of soil erosion changes over 7 years due to mining operations in two neighboring hilltops in West-Singhbhum District, Jharkhand, are reported. CartoSat-1, ETMþ and LISS-IV satellites' data provided spatial inputs in Universal Soil Loss Equation (USLE) and Morgan, (2008), which shows that both models predicted significantly differently as a result of the different factors considered. Overall, the MMF model predicted a higher soil erosion rate but less variation than USLE. Both models showed soil erosion rates were drastically increased by anthropogenic activities in the area, hence careful consideration is needed. The same sensor and imaging data could not be maintained. Correction of errors may reduce erosion, but it will still remain significant for future planning.

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